利用快速分组LARS对多个aCGH轮廓进行联合分割
Joint segmentation of many aCGH profiles using fast group LARS
摘要：
基于阵列的比较基因组杂交（aCGH）是用于搜索具有拷贝数变异的基因组区域的方法。对于给定的aCGH谱，一个挑战是将其准确地分割成恒定拷贝数的区域。共享相同疾病状态（例如一种类型的癌症）的受试者通常具有由于与该特定疾病相关的重复和缺失而具有相似拷贝数变异的aCGH谱。我们引入了一个约束优化算法，联合分割多个科目的aCGH配置文件。它同时惩罚了一组谱必须从一个恒定拷贝数水平跳到另一个水平的自由度，在基因组位置被称为断点。我们发现，许多不同的配置文件共享的断点往往首先被发现的算法，即使在存在大量的噪声。该算法可以表述为一组LARS问题。我们提出了一种非常快速的方法来找到解决方案的路径，即，按重要性排序的一系列共享断点。在没有额外成本的情况下，该算法将所有aCGH轮廓平滑成相等拷贝数的分段恒定区域，从而给出原始数据的低维版本。这些可以显示在一个单一的图形上的所有配置文件，允许直观的视觉解释。提供了对膀胱癌aCGH谱的算法的模拟和实施。
英文摘要：
Array-Based Comparative Genomic Hybridization (aCGH) is a method used to search for genomic regions with copy numbers variations. For a given aCGH profile, one challenge is to accurately segment it into regions of constant copy number. Subjects sharing the same disease status, for example a type of cancer, often have aCGH profiles with similar copy number variations, due to duplications and deletions relevant to that particular disease. We introduce a constrained optimization algorithm that jointly segments aCGH profiles of many subjects. It simultaneously penalizes the amount of freedom the set of profiles have to jump from one level of constant copy number to another, at genomic locations known as breakpoints. We show that breakpoints shared by many different profiles tend to be found first by the algorithm, even in the presence of significant amounts of noise. The algorithm can be formulated as a group LARS problem. We propose an extremely fast way to find the solution path, i.e., a sequence of shared breakpoints in order of importance. For no extra cost the algorithm smoothes all of the aCGH profiles into piecewise-constant regions of equal copy number, giving low-dimensional versions of the original data. These can be shown for all profiles on a single graph, allowing for intuitive visual interpretation. Simulations and an implementation of the algorithm on bladder cancer aCGH profiles are provided.
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